Generative AI as a Tool for Environmental Health Research Translation

Practices and Barriers in Developing and Disseminating Plain-Language Resources Reporting Medical Research Information: A Scoping Review

BACKGROUND

The intent of plain-language resources (PLRs) reporting medical research information is to advance health literacy among the general public and enable them to participate in shared decision-making (SDM). Regulatory mandates coupled with academic and industry initiatives have given rise to an increasing volume of PLRs summarizing medical research information. However, there is significant variability in the quality, format, readability, and dissemination channels for PLRs. In this scoping review, we identify current practices, guidance, and barriers in developing and disseminating PLRs reporting medical research information to the general public including patients and caregivers. We also report on the PLR preferences of these intended audiences.

METHODS

A literature search of three bibliographic databases (PubMed, EMBASE, Web of Science) and three clinical trial registries (NIH, EMA, ISRCTN registry) was performed. Snowball searches within reference lists of primary articles were added. Articles with PLRs or reporting topics related to PLRs use and development available between January 2017 and June 2023 were identified. Evidence mapping and synthesis were used to make qualitative observations. Identified PLRs were quantitatively assessed, including temporal annual trends, availability by field of medicine, language, and publisher types.

RESULTS

A total of 9116 PLRs were identified, 9041 from the databases and 75 from clinical trial registries. The final analysis included 6590 PLRs from databases and 72 from registries. Reported barriers to PLR development included ambiguity in guidance, lack of incentives, and concerns of researchers writing for the general public. Available guidance recommendations called for greater dissemination, increased readability, and varied content formats. Patients preferred visual PLRs formats (e.g., videos, comics), which were easy to access on the internet and used short jargon-free text. In some instances, older audiences and more educated readers preferred text-only PLRs. Preferences among the general public were mostly similar to those of patients. Psychology, followed by oncology, showed the highest number of PLRs, predominantly from academia-sponsored research. Text-only PLRs were most commonly available, while graphical, digital, or online formats were less available. Preferred dissemination channels included paywall-free journal websites, indexing on PubMed, third-party websites, via email to research participants, and social media.

CONCLUSIONS

This scoping review maps current practices, recommendations, and patients' and the general public's preferences for PLR development and dissemination. The results suggest that making PLRs available to a wider audience by improving nomenclature, accessibility, and providing translations may contribute to empowerment and SDM. Minimizing variability among available guidance for PLR development may play an important role in amplifying the value and impact of these resources.

The diagnostic, therapeutic, and ethical impact of artificial intelligence in modern medicine

In the evolution of modern medicine, artificial intelligence (AI) has been proven to provide an integral aspect of revolutionizing clinical diagnosis, drug discovery, and patient care. With the potential to scrutinize colossal amounts of medical data, radiological and histological images, and genomic data in healthcare institutions, AI-powered systems can recognize, determine, and associate patterns and provide impactful insights that would be strenuous and challenging for clinicians to detect during their daily clinical practice. The outcome of AI-mediated search offers more accurate, personalized patient diagnoses, guides in research for new drug therapies, and provides a more effective multidisciplinary treatment plan that can be implemented for patients with chronic diseases. Among the many promising applications of AI in modern medicine, medical imaging stands out distinctly as an area with tremendous potential. AI-powered algorithms can now accurately and sensitively identify cancer cells and other lesions in medical images with greater accuracy and sensitivity. This allows for earlier diagnosis and treatment, which can significantly impact patient outcomes. This review provides a comprehensive insight into diagnostic, therapeutic, and ethical issues with the advent of AI in modern medicine.

Publishing clinical trial results in plain language: a clash of ethical principles?

Plain language resources (PLR) are lay summaries of clinical trial results or plain language summaries of publications, in digital/visual/language formats. They aim to provide accurate information in jargon-free, and easy-to-understand language that can meet the health information needs of the general public, especially patients and caregivers. These are typically developed by the study sponsors or investigators, or by national public health bodies, research hospitals, patient organizations, and non-profit organizations. While the usefulness of PLR seems unequivocal, they have never been analyzed from the perspective of ethics. In this commentary, we do so and reflect on whether PLR are categorically advantageous or if they solve certain issues but raise new problems at the same time. Ethical concerns that PLR can potentially address include but are not limited to individual and community level health literacy, patient empowerment and autonomy. We also highlight the ethical issues that PLR may potentially exacerbate, such as fair balanced presentation and interpretation of medical knowledge, positive publication bias, and equitable access to information. PLR are important resources for patients, with promising implications for individual as well as community health. However, they require appropriate oversight and standards to optimize their potential value. Hence, we also highlight recommendations and best practices from our reading of the literature, that aim to minimize these biases.

Perception of Race and Sex Diversity in Ophthalmology by Artificial Intelligence: A DALL E-2 Study

Purpose

In the past few years, there has been remarkable progress in accessibility of open-source artificial intelligence (AI) image generators, developed to help humans understand how AI sees our world. Here, we characterize perception of racial and sex diversity in ophthalmology by AI.

Methods

OpenAI's open-source DALL E-2 AI was used for image generation of ophthalmologists with queries that all included "American" and "portrait photo". Factors used for queries contained categories of following: "Positive Characteristic", "Negative Characteristic", "Finances", "Region", "Experience", "Academic Rank", and "Subspecialty". The first 40 faces for each search were categorized on the basis of race and sex by two independent reviewers. If race or sex was not agreed upon, a third reviewer independently provided a classification, or if still indeterminate, the image was labeled as such. Images that did not adequately show facial features were excluded from categorization.

Results

A total of 1560 images were included in the analysis. Control search queries specifying solely ophthalmologist sex and/or race outputted (100%) accurate images validating the tool. The query "American ophthalmologist, portrait photo" portrayed the majority of ophthalmologists as White (75%) and male (77.5%). Young/inexperienced/amateur ophthalmologists were perceived to have greater non-White racial diversity (27.5%) and female representation (28.3%) relative to old/experienced/mature ophthalmologists (23.3% non-White and 18.3% female). Ophthalmology department chairs (25%) had slightly more racial diversity compared to residents (22.5%), but residents had greater female representation (30%) compared to chairs (15%).

Conclusion

Our results suggest the DALL E-2 AI may perceive a trend of increasing racial and sex diversity in younger, newer ophthalmologists compared to more senior ophthalmologists. Future investigations should attempt to validate how AI may be used as a tool to evaluate ophthalmology's progress towards becoming more inclusive of increasingly diverse ophthalmologists.